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E-raamat: Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making

Edited by (Florida International University, USA), Edited by (University of Vaasa, Finland)
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  • Ilmumisaeg: 14-Dec-2021
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119748328
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Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision MakingSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 14-Dec-2021
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119748328
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"This book introduces novel algorithms and solutions to real-world problems under the umbrella of cyberphysical systems. It is organized in two sections: the first covers optimization algorithms for large-scale decision-making and the second covers intelligent decision-making in cyberphysical smart cities. The book takes into account new directions in engineering and science by deploying novel efficient algorithms to enhance near-real-time operation of underlying networks and use of peer-to-peer communication. These include the more in-depth study of special issues on deployment of these algorithms to improve the operation of smart cities. The material is presented in a concise and understandable form, taking into account the requirements for technical texts"--

Learn to deploy novel algorithms to improve and secure smart city infrastructure

In Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making, accomplished researchers Drs. M. Hadi Amini and Miadreza Shafie-Khah deliver a crucial exploration of new directions in the science and engineering of deploying novel and efficient computing algorithms to enhance the efficient operation of the networks and communication systems underlying smart city infrastructure. The book covers special issues on the deployment of these algorithms with an eye to helping readers improve the operation of smart cities.

The editors present concise and accessible material from a collection of internationally renowned authors in areas as diverse as computer science, electrical engineering, operation research, civil engineering, and the social sciences. They also include discussions of the use of artificial intelligence to secure the operations of cyberphysical smart city infrastructure and provide several examples of the applications of novel theoretical algorithms.

Readers will also enjoy:

  • Thorough introductions to fundamental algorithms for computing and learning, large-scale optimizations, control theory for large-scale systems
  • Explorations of machine learning and intelligent decision making in cyberphysical smart cities, including smart energy systems and intelligent transportation networks
  • In-depth treatments of intelligent decision making in cyberphysical smart city infrastructure and optimization in networked smart cities

Perfect for senior undergraduate and graduate students of electrical and computer engineering, computer science, civil engineering, telecommunications, information technology, and business, Cyberphysical Smart Cities Infrastructures is an indispensable reference for anyone seeking to solve real-world problems in smart cities.

Biography xv
List of Contributors
xvii
1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications
1(12)
Aaron Turransky
M. Hadi Amini
1.1 Introduction
1(1)
1.2 A Brief History of AI
2(2)
1.3 AI in Healthcare
4(2)
1.4 Morality and Ethical Association of AI in Healthcare
6(1)
1.5 Cybersecurity, AI, and Healthcare
7(2)
1.6 Future of AI and Healthcare
9(1)
1.7 Conclusion
10(3)
References
11(2)
2 Data Analytics for Smart Cities: Challenges and Promises
13(16)
Ghareh Mohammadi
Farzan Shenavarmasouleh
M. Hadi Amini
Hamid Reza Arabnia
2.1 Introduction
13(2)
2.2 Role of Machine Learning in Smart Cities
15(1)
2.3 Smart Cities Data Analytics Framework
16(7)
2.3.1 Data Capturing
16(2)
2.3.2 Data Analysis
18(1)
2.3.2.1 Big Data Algorithms and Challenges
18(1)
2.3.2.2 Machine Learning Process and Challenges
19(1)
2.3.2.3 Deep Learning Process and Challenges
19(1)
2.3.2.4 Learning Process and Emerging New Type of Data Problems
20(1)
2.3.3 Decision-Making Problems in Smart Cities
21(1)
2.3.3.1 Traffic Decision-Making System
21(1)
2.3.3.2 Safe and Smart Environment
22(1)
2.4 Conclusion
23(6)
References
23(6)
3 Embodied Al-Driven Operation of Smart Cities: A Concise Review
29(18)
Farzan Shenavarmasouleh
Ghareh Mohammadi
M. Hadi Amini
Hamid Reza Arabnia
3.1 Introduction
29(2)
3.2 Rise of the Embodied AI
31(2)
3.3 Breakdown of Embodied AI
33(3)
3.3.1 Language Grounding
33(1)
3.3.2 Language Plus Vision
33(1)
3.3.3 Embodied Visual Recognition
34(1)
3.3.4 Embodied Question Answering
35(1)
3.3.5 Interactive Question Answering
35(1)
3.3.6 Multi-agent Systems
35(1)
3.4 Simulators
36(2)
3.4.1 MINOS
37(1)
3.4.2 Habitat
38(1)
3.5 Future of Embodied AI
38(1)
3.5.1 Higher Intelligence
38(1)
3.5.2 Evolution
39(1)
3.6 Conclusion
39(8)
References
39(8)
4 Analysis of Different Regression Techniques for Battery Capacity Prediction
47(14)
Param Khakhar
Rahul Kumar Dubey
4.1 Introduction
47(1)
4.2 Data Preparation
48(4)
4.2.1 Dataset
48(1)
4.2.2 Feature Extraction
49(3)
4.2.3 Noise Addition
52(1)
4.3 Experiment Design and Machine Learning Algorithms
52(1)
4.4 Result and Analysis
53(4)
4.5 Threats to Validity
57(1)
4.6 Conclusions
58(3)
References
59(2)
5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City
61(34)
Milad Kazemi
Samuel Bailey
Sadegh Soudjani
Vahid Vahidinasab
5.1 Smart Charging in Transportation
61(4)
5.1.1 Available EV Charging Technologies
61(1)
5.1.1.1 Inductive Charging
61(1)
5.1.1.2 Battery Swapping
62(1)
5.1.1.3 Automatic Robotic Charging Connector
62(1)
5.1.1.4 Automatic Ground-Based Docking Connector
62(1)
5.1.2 Current Regulations on Smart Charging
62(3)
5.2 Cyber-Physical Aspects of EV Networks
65(3)
5.2.1 Sensing and Cooperative Data Collection
65(2)
5.2.2 Data-Driven Control and Optimization
67(1)
5.3 Charging of Electric Fleet Vehicles in Smart Cities
68(3)
5.3.1 Intelligent Management of Fleets of Electric Vehicles
68(1)
5.3.1.1 Charging of EV Fleets
68(1)
5.3.1.2 Route Mapping with Charging
69(1)
5.3.2 Electricity Grid Support Services
70(1)
5.3.2.1 Demand Response
70(1)
5.3.2.2 Frequency Response
70(1)
5.3.2.3 Emergency Power
71(1)
5.3.2.4 Emergency Response
71(1)
5.4 Data and Cyber Security of EV Networks
71(6)
5.4.1 Attack Schemes
71(1)
5.4.1.1 Data Injection
72(1)
5.4.1.2 Distributed Denial of Service
72(1)
5.4.1.3 Data and Identity Theft
72(1)
5.4.1.4 Man-in-the-Middle Attack
73(1)
5.4.2 Attack Detection Methods
74(1)
5.4.2.1 Abnormal State Estimation
74(1)
5.4.2.2 Message Encryption and Authentication
75(1)
5.4.2.3 Denial-of-Service Attacks
75(1)
5.4.3 Privacy Concerns and Privacy-Preserving Methods
76(1)
5.5 EV Smart Charging Strategies
77(9)
5.5.1 Optimization Approaches
77(1)
5.5.1.1 Future Scheduling
77(1)
5.5.1.2 Battery Health Optimization
78(1)
5.5.1.3 Energy Loss Minimization
78(1)
5.5.2 Artificial Intelligence Approaches
79(1)
5.5.2.1 Deep Learning for Smart Charging
79(1)
5.5.2.2 Predicting Charging Profiles
79(1)
5.5.3 Coordinated Charging
80(1)
5.5.3.1 Centralized Optimization
80(1)
5.5.3.2 Distributed Optimization
81(1)
5.5.4 Population-Based Approaches
82(1)
5.5.4.1 Case Study
83(3)
5.6 Conclusion
86(9)
Acknowledgments
88(1)
References
88(7)
6 Risk-Aware Cyber-Physical Control for Resilient Smart Cities
95(28)
Eman Hammad
Abdattah Farraj
6.1 Introduction
95(2)
6.2 System Model
97(6)
6.2.1 Communication Latency in Smart Grid Systems
98(1)
6.2.2 Risk Model for Communication Links
99(2)
6.2.3 History of Communication Links
101(2)
6.3 Risk-Aware Quality of Service Routing Using SDN
103(6)
6.3.1 Constrained Shortest Path Routing Problem Formulation
103(2)
6.3.2 SDN Architecture and Implementation
105(1)
6.3.3 Risk-Aware Routing Algorithm
106(3)
6.4 Risk-Aware Adaptive Control
109(2)
6.4.1 Smart Grid Model
109(1)
6.4.2 Parametric Feedback Linearization Control
110(1)
6.4.3 Risk-Aware Routing and Latency-Adaptive Control Scheme
111(1)
6.5 Simulation Environment and Numerical Analysis
111(7)
6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint
113(2)
6.5.2 Algorithm Overhead Comparison
115(1)
6.5.3 Impact of QoS Constraints
116(1)
6.5.4 Impact on Distributed Control
116(2)
6.6 Conclusions
118(5)
References
119(4)
7 Wind Speed Prediction Using a Robust Possibilistic C-Regression Model Method: A Case Study of Tunisia
123(16)
Achraf J. Tetmoudi
Moez Sottani
Lotfi Chaouech
Abdelkader Chaari
7.1 Introduction
123(2)
7.2 Data Collection and Method
125(3)
7.2.1 Data Description
125(1)
7.2.2 Robust Possibilistic C-Regression Models
125(3)
7.2.3 Wind Speed Data Analysis Procedure
128(1)
7.3 Experiment and Discussion
128(6)
7.4 Conclusion
134(5)
References
136(3)
8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection
139(28)
Gabrielle Bakker-Reynolds
Emre Erturk
Istvan Lengyel
8.1 Introduction
139(3)
8.1.1 Introduction
139(1)
8.1.2 Background
140(1)
8.1.3 Problem Statement
140(1)
8.1.3.1 Purpose of Research
140(1)
8.1.3.2 Research Questions
140(1)
8.1.3.3 Study Aim and Objectives
141(1)
8.1.3.4 Significance and Structure of the Research
141(1)
8.2 Literature Review
142(9)
8.2.1 Introduction
142(1)
8.2.2 Machine Learning, Deep Learning, and Computer Vision
142(1)
8.2.2.1 Machine Learning
142(1)
8.2.2.2 Deep Learning
143(1)
8.2.2.3 Computer Vision
144(1)
8.2.3 Object Recognition, Object Detection, and Object Tracking
144(1)
8.2.3.1 Object Recognition
144(1)
8.2.3.2 Object Detection
145(1)
8.2.3.3 Object Tracking
146(1)
8.2.4 Edge Computing, Fog Computing, and Cloud Computing
146(1)
8.2.4.1 Edge Computing
146(1)
8.2.4.2 Fog Computing
147(1)
8.2.4.3 Cloud Computing
147(1)
8.2.5 Benefits of Computer Vision-Driven Traffic Management
147(1)
8.2.6 Challenges of Computer Vision-Driven Traffic Management
148(1)
8.2.6.1 Big Data Issues
148(1)
8.2.6.2 Privacy Issues
149(1)
8.2.6.3 Technical Barriers
150(1)
8.3 Research Methodology
151(8)
8.3.1 Research Questions and Objectives
151(1)
8.3.2 Study Design
151(1)
8.3.2.1 Selection Rationale
152(1)
8.3.2.2 Potential Challenges
152(1)
8.3.3 Adapted Study Design Research Approach
153(1)
8.3.4 Selected Hardware and Software
154(1)
8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items
155(2)
8.3.5 Hardware Proposed
157(1)
8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations)
157(1)
8.3.6 Software Proposed
157(2)
8.4 Conclusion
159(8)
References
160(7)
9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection
167(30)
Gabrielle Bakker-Reynolds
Emre Erturk
Istvan Lengyel
Noor Alani
9.1 Prototype Setup
167(2)
9.1.1 Introduction
167(1)
9.1.2 Environment Setup
168(1)
9.2 Testing
169(6)
9.2.1 Design and Development: The Default Model and the First Iteration
169(1)
9.2.2 Testing (Multiple Images)
170(1)
9.2.3 Analysis (Multiple Images)
170(1)
9.2.4 Testing (MP4 File)
171(3)
9.2.5 Testing (Livestream Camera)
174(1)
9.3 Iteration 2: Transfer Learning Model
175(8)
9.3.1 Design and Development
175(4)
9.3.2 Test (Multiple Images)
179(1)
9.3.3 Analysis (Multiple Images)
179(1)
9.3.4 Test (MP4 File)
179(2)
9.3.5 Analysis (MP4 File)
181(1)
9.3.6 Test (Livestream Camera)
181(1)
9.3.7 Analysis (Livestream Camera)
181(1)
9.3.8 Redesign
182(1)
9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images)
183(3)
9.4.1 Design and Development
183(1)
9.4.2 Testing
184(1)
9.4.3 Analysis
184(1)
9.4.3.1 Confusion Matrices
184(1)
9.4.3.2 Precision, Recall, and F-score
185(1)
9.5 Findings and Discussion
186(7)
9.5.1 Findings: Vehicle Detection Across Multiple Images
186(1)
9.5.2 Findings: Vehicle Detection Performance on an MP4 File
187(1)
9.5.3 Findings: Vehicle Detection on Livestream Camera
188(1)
9.5.4 Findings: Iteration 3
189(1)
9.5.5 Addressing the Research Questions
190(1)
9.5.6 Assessment of Suitability
191(1)
9.5.7 Future Improvements
192(1)
9.6 Conclusion
193(4)
References
194(3)
10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications
197(58)
Youcef Himri
S. M. Muyeen
Farhan Hameed Malik
Saliha Himri
Khairol Amali bin Ahmad
Nachida Kasbadji Merzouk
Mustapha Merzouk
10.1 Introduction
197(1)
10.2 Overview of IEC 61850 Standards
198(1)
10.3 IEC 61850 Protocols and Substandards
199(8)
10.3.1 IEC 61850 Standards and Classifications
199(4)
10.3.2 Basics of IEC 61850 Architecture Model
203(1)
10.3.3 IEC 61850 Class Model
203(3)
10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850)
206(1)
10.4 IEC 61850 Features
207(9)
10.4.1 MMS
208(1)
10.4.2 GOOSE
208(1)
10.4.3 Sampled Measured Value (SMV) or SV
209(1)
10.4.4 R-GOOSE and R-SV
210(1)
10.4.4.1 Application in Transmission Systems
211(3)
10.4.4.2 Application in Distribution Systems
214(2)
10.4.5 Web Services
216(1)
10.5 Relevant Application
216(17)
10.5.1 Substation Automation System (SAS)
216(1)
10.5.2 Energy Management System (EMS)
217(2)
10.5.3 Distribution Management System (DMS)
219(1)
10.5.3.1 Feeder Balancing and Loss Minimization Distribution
219(1)
10.5.3.2 Voltage/VAR Optimization (WO) and Conservation Voltage Reduction
220(1)
10.5.3.3 Fault Location, Isolation, and Service Restoration
220(1)
10.5.4 Distribution Automation (DA)
220(1)
10.5.4.1 Voltage/VAR Control
221(1)
10.5.4.2 Fault Detection and Isolation
221(1)
10.5.4.3 Service Restoration Use Case
221(1)
10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [ DER])
222(1)
10.5.5.1 Storage
222(2)
10.5.5.2 Solar Panels
224(2)
10.5.5.3 Wind Farm
226(1)
10.5.5.4 Virtual Power Plant (VPP)
226(4)
10.5.6 Advanced Metering Infrastructure (AMI)
230(1)
10.5.7 Electric Vehicle (EV)
230(3)
10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850)
233(6)
10.6.1 Communications Bandwidth
233(1)
10.6.2 Interoperability
233(1)
10.6.3 Cybersecurity
234(1)
10.6.4 Information Security and Privacy
235(1)
10.6.5 Security Aspects of IEC 61850 in Smart Grid Applications
235(1)
10.6.5.1 How Security Can Be Breached
235(1)
10.6.5.2 Effects on the Security
236(1)
10.6.5.3 Efforts to Address the Security Issues
236(2)
10.6.6 Reliability of Technology
238(1)
10.7 Conclusion and Perspective
239(16)
Acknowledgments
240(1)
References
240(15)
11 Electric Vehicles in Smart Cities
255(29)
Sahand G. Liasi
Mohammad T. Bina
11.1 Introduction
255(1)
11.2 Autonomous Vehicle
255(4)
11.2.1 Classification of Vehicle Automation
257(1)
11.2.2 Advantages of CAEVs
258(1)
11.3 IoT Technology and CAEV
259(2)
11.4 CAEV Applications and Services
261(3)
11.4.1 CAEV Charging Management System
262(1)
11.4.2 CAEV Taxi Service
263(1)
11.5 Cybersecurity Issues of Internet of EVs
264(8)
11.5.1 UPCEV Architecture
265(1)
11.5.2 IoV Communication Technologies
266(2)
11.5.3 IoEV Vulnerabilities
268(1)
11.5.3.1 V2S Attacks
268(1)
11.5.3.2 V2V Attacks
269(2)
11.5.3.3 V2I Attack
271(1)
11.5.3.4 V2N Attacks
272(1)
11.6 IoT-Based EV State Estimation and Control Under Cyberattacks
272(6)
11.6.1 State Estimation Problem
273(1)
11.6.2 Vehicle State Space and IoT Sensing Systems
274(1)
11.6.3 State Estimation Under Cyberattack
275(3)
11.7 Effect of EV Charging Behavior on Power System
278(3)
11.7.1 Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
279(1)
11.7.2 Parking Lots in Demand Response Programs
280(1)
11.7.3 Application of IoT in Demand Response Programs Based on Parking Lots
281(1)
11.8 Charging Scheme for EVs Using IoT
281(3)
11.8.1 System Model and IoT Architecture
282(2)
References 284(3)
Author Index 287
M. Hadi Amini, PhD, DEng, is Assistant Professor at the Knight Foundation School of Computing and Information Sciences, College of Engineering and Computing at Florida International University. He is also the Founding Director of Sustainability, Optimization, and Learning for InterDependent Networks Laboratory (www.solidlab.network).

Miadreza Shafie-Khah, PhD, is Associate Professor at the University of Vaasa, Finland. He is Editor-in-Chief and Editor of several journals, Senior Member of IEEE and in the Steering Committee of Diverse International Conferences.
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